553 research outputs found

    A local maximum principle for robust optimal control problems of quadratic BSDEs

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    The paper concerns the necessary maximum principle for robust optimal control problems of quadratic BSDEs. The coefficient of the systems depends on the parameter θ\theta, and the generator of BSDEs is of quadratic growth in zz. Since the model is uncertain, the variational inequality is proved by weak convergence technique. In addition, due to the generator being quadratic with respect to zz, the forward adjoint equations are SDEs with unbounded coefficient involving mean oscillation martingales. Using reverse H\"older inequality and John-Nirenberg inequality, we show that its solutions are continuous with respect to the parameter θ\theta. The necessary and sufficient conditions for robust optimal control are proved by linearization method.Comment: 35 page

    Data fusion technology for precision forestry applications

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    Presently precision forestry is playing an important role in realizing sustainable development and improving societal and economical efficiency for forestry applications. Based on analyzing the features of precision forestry's information requirements, the data needed for precision forestry were classified and the characteristics of the different information were summarized. Data fusion for precision forestry was studied in this paper. The architecture for precision forestry information processing, which integrated information fusion and data mining, was put forward. New and emerging technologies such as Remote Sensing (RS), Geographical Information System (GIS), Global Position System (GPS), Data Base Management System (DBMS), Data Fusion, Decision Support Systems (DSS), and Variable Rate technology (VRT) are applied in forestry production as aids in producers' and managers' decision-making process. Precision irrigation, precision fertilizing, precision pesticide application, precision harvesting, and precision deforestation can promote the realization of minimizing resource inputs, minimizing environmental impacts, and maximizing forest outputs

    Comparison of DDS, MQTT, and Zenoh in Edge-to-Edge and Edge-to-Cloud Communication for Distributed ROS 2 Systems

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    The increased data transmission and number of devices involved in communications among distributed systems make it challenging yet significantly necessary to have an efficient and reliable networking middleware. In robotics and autonomous systems, the wide application of ROS\,2 brings the possibility of utilizing various networking middlewares together with DDS in ROS\,2 for better communication among edge devices or between edge devices and the cloud. However, there is a lack of comprehensive communication performance comparison of integrating these networking middlewares with ROS\,2. In this study, we provide a quantitative analysis for the communication performance of utilized networking middlewares including MQTT and Zenoh alongside DDS in ROS\,2 among a multiple host system. For a complete and reliable comparison, we calculate the latency and throughput of these middlewares by sending distinct amounts and types of data through different network setups including Ethernet, Wi-Fi, and 4G. To further extend the evaluation to real-world application scenarios, we assess the drift error (the position changes) over time caused by these networking middlewares with the robot moving in an identical square-shaped path. Our results show that CycloneDDS performs better under Ethernet while Zenoh performs better under Wi-Fi and 4G. In the actual robot test, the robot moving trajectory drift error over time (96\,s) via Zenoh is the smallest. It is worth noting we have a discussion of the CPU utilization of these networking middlewares and the performance impact caused by enabling the security feature in ROS\,2 at the end of the paper.Comment: 19 pages, 8 figures. Submitted to the Journal of Intelligent & Robotic Systems. Under revie

    Co-administration of a DNA vaccine encoding the prostate specific membrane antigen and CpG oligodeoxynucleotides suppresses tumor growth

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    BACKGROUND: Prostate-specific membrane antigen (PSMA) is a well characterized prostate-specific tumor associated antigen. Its expression is elevated in prostate carcinoma, particularly in metastatic and recurrent lesions. These observations suggest that PSMA can be used as immune target to induce tumor cell-specific recognition by the host and, consequently tumor rejection. We utilized a DNA-based vaccine to specifically enhance PSMA expression. An immune modulator, such as CpG oligodeoxynucleotides which promote Th1-type immune responses was combined to increase the efficacy of tumor recognition and elimination. METHODS: A eukaryotic expression plasmid pCDNA3.1-PSMA encoding full-length PSMA was constructed. C57BL/6 mice were immunized with endotoxin-free pCDNA3.1-PSMA alone or in combination with CpG oligodeoxynucleotides by intramuscular injection. After 4 immunizations, PSMA specific antibodies and cytotoxic T lymphocyte reactivity were measured. Immunized C57BL/6 mice were also challenged subcutaneously with B16 cells transfected with PSMA to evaluate suppression of tumor growth. RESULTS: Vaccine-specific cytotoxic T lymphocytes reactive with B16 cells expressing PSMA could be induced with this treatment schedule. Immune protection was observed in vaccinated mice as indicated by increased tumor growth in the control group (100%) compared with the groups vaccinated with DNA alone (66.7%) or DNA plus CpG oligodeoxynucleotides (50%) respectively. Average tumor volume was smaller in vaccinated groups and tumor-free survival time was prolonged by the vaccination. CONCLUSION: The current findings suggest that specific anti-tumor immune response can be induced by DNA vaccines expressing PSMA. In addition, the suppression of in vivo growth of tumor cells expressing PSMA was augmented by CpG oligodeoxynucleotides. This strategy may provide a new venue for the treatment of carcinoma of prostate after failure of standard therapy

    Nerve block reduces the incidence of 3-year postoperative mortality: a retrospective cohort study

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    PurposeA retrospective cohort study was performed to determine the effect of nerve block on the incidence of postoperative mortality in patients with hip replacement.MethodsAccording to the inclusion and exclusion criteria, patients who were undergoing hip replacement for the first time under general or intraspinal anesthesia, classified as ASA class I–IV, and aged ≥65 years were selected. We collected the general data, past medical history, preoperative laboratory test results, perioperative fluid intake and outflow data, perioperative anesthesia and related drug data, postoperative laboratory results, and correlation time index. Patients with preoperative combined nerve block were included in the N group, and those without combined nerve block were included in the NN group. The patients were followed up via telephone call to assess survival outcomes at 3 years after surgery. Propensity score matching and uni- and multivariate analyses were performed to determine the influence of nerve block and other related factors on postoperative mortality.ResultsA total of 743 patients were included in this study, including 262 in the N group and 481 in the NN group. Two hundred five patients in both groups remained after propensity score matching. Main result: Preoperative nerve block was associated with reduced mortality three years after surgery.ConclusionNerve block reduces the incidence of 3-year postoperative mortality, and composite nerve block with general anesthesia and neuraxial anesthesia is worthy of promotion

    Distributed Robotic Systems in the Edge-Cloud Continuum with ROS 2: a Review on Novel Architectures and Technology Readiness

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    Robotic systems are more connected, networked, and distributed than ever. New architectures that comply with the \textit{de facto} robotics middleware standard, ROS\,2, have recently emerged to fill the gap in terms of hybrid systems deployed from edge to cloud. This paper reviews new architectures and technologies that enable containerized robotic applications to seamlessly run at the edge or in the cloud. We also overview systems that include solutions from extension to ROS\,2 tooling to the integration of Kubernetes and ROS\,2. Another important trend is robot learning, and how new simulators and cloud simulations are enabling, e.g., large-scale reinforcement learning or distributed federated learning solutions. This has also enabled deeper integration of continuous interaction and continuous deployment (CI/CD) pipelines for robotic systems development, going beyond standard software unit tests with simulated tests to build and validate code automatically. We discuss the current technology readiness and list the potential new application scenarios that are becoming available. Finally, we discuss the current challenges in distributed robotic systems and list open research questions in the field
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